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Dr.Quad at MEDIQA 2019: Towards Textual Inference and Question Entailment using contextualized representations

2019-07-23 · WS 2019 8 · Vinayshekhar Bannihatti Kumar, Ashwin Srinivasan, Aditi Chaudhary, James Route, Teruko Mitamura, Eric Nyberg

This paper presents the submissions by Team Dr.Quad to the ACL-BioNLP 2019 shared task on Textual Inference and Question Entailment in the Medical Domain. Our system is based on the prior work Liu et al. (2019) which uses a multi-task objective function for textual entailment. In this work, we explore different strategies for generalizing state-of-the-art language understanding models to the specialized medical domain. Our results on the shared task demonstrate that incorporating domain knowledge through data augmentation is a powerful strategy for addressing challenges posed by specialized domains such as medicine.

📄 PDF Abstract BibTeX arXiv:1907.10136

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Tasks

Data Augmentation Natural Language Inference

Overview of the MEDIQA 2019 Shared Task on Textual Inference, Question Entailment and Question Answering

2019-08-01 · WS 2019 8 · Asma Ben Abacha, Chaitanya Shivade, Dina Demner-Fushman

This paper presents the MEDIQA 2019 shared task organized at the ACL-BioNLP workshop. The shared task is motivated by a need to develop relevant methods, techniques and gold standards for inference and entailment in the …

Information Retrieval Natural Language Inference Question Answering Retrieval

ANU-CSIRO at MEDIQA 2019: Question Answering Using Deep Contextual Knowledge

2019-08-01 · WS 2019 8 · Vincent Nguyen, Sarvnaz Karimi, Zhenchang Xing

We report on our system for textual inference and question entailment in the medical domain for the ACL BioNLP 2019 Shared Task, MEDIQA. Textual inference is the task of finding the semantic relationships between pairs o…

Medical Question Answering Natural Language Inference Question Answering

UU\_TAILS at MEDIQA 2019: Learning Textual Entailment in the Medical Domain

2019-08-01 · WS 2019 8 · Noha Tawfik, Marco Spruit

This article describes the participation of the UU{\_}TAILS team in the 2019 MEDIQA challenge intended to improve domain-specific models in medical and clinical NLP. The challenge consists of 3 tasks: medical language in…

Natural Language Inference Question Answering Sentence

ARS\_NITK at MEDIQA 2019:Analysing Various Methods for Natural Language Inference, Recognising Question Entailment and Medical Question Answering System

2019-08-01 · WS 2019 8 · Anumeha Agrawal, Rosa Anil George, Selvan Suntiha Ravi, Sowmya Kamath S 외

In this paper, we present three approaches for Natural Language Inference, Question Entailment Recognition and Question-Answering to improve domain-specific Information Retrieval. For addressing the NLI task, the UMLS Me…

Information Retrieval Medical Question Answering Natural Language Inference Question Answering+2

Sieg at MEDIQA 2019: Multi-task Neural Ensemble for Biomedical Inference and Entailment

2019-08-01 · WS 2019 8 · Sai Abishek Bhaskar, Rashi Rungta, James Route, Eric Nyberg 외

This paper presents a multi-task learning approach to natural language inference (NLI) and question entailment (RQE) in the biomedical domain. Recognizing textual inference relations and question similarity can address t…

Multi-Task Learning Natural Language Inference Question Similarity